Impact of Augmentation on Batik Classification using Convolution Neural Network and K-Neareast Neighbor

With the gradual improvement of people's living standards, the production and drinking of all kinds of food is increasing. People's disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

The aim of this study was to investigate the value of single-photon emission computed tomography (SPECT) based on the convolutional neural network (CNN) algorithm in thyroid diseases. Thirty-five patients with thyroid disease from the hospital were selected as the observation group, and another 35 healthy volunteers were selected as the control group. The constructed model of SPECT based on the CNN algorithm was compared with the backpropagation neural network (BPNN) algorithm, which was then applied to the SPECT of 35 patients with thyroid disease. It turned out that as the number of iterations increased, the parameter training of CNN was gradually sufficient, the network model was continuously optimized, and the accuracy gradually increased. From the data results, the Dice value of the proposed CNN algorithm was higher than that of the BPNN algorithm and the segmentation effect was relatively good. The visual index of the thyroid/neck of the observation group (2.68 ± 1.32) was remarkably inferior to that of the control group (12.347.54) ( P < 0.05 ). The visual index of the thyroid/submandibular gland in the observation group (1.02 ± 0.41) was remarkably inferior to that of the control group (8.89 ± 4.86) ( P < 0.05 ). The visual index of the thyroid/parotid gland in the observation group (1.04 ± 0.58) was remarkably inferior to that of the control group (8.53 ± 4.25) ( P < 0.05 ). In addition, 99mTcO4-SPECT had a sensitivity of 95.2%, a specificity of 90.3%, and an accuracy of 91.5% in the diagnosis of thyroid diseases. The area under the curve of the receiver operating characteristic curve for 99mTcO4-SPECT diagnosis of thyroid disease is 0.958, and the 95% confidence interval is 0.834∼1. In summary, the SPECT based on the CNN algorithm proposed in this study has a good segmentation effect and can accurately locate the anatomical information of thyroid diseases, which can replace the traditional diagnostic methods for the diagnosis of thyroid diseases.

Retinal fundus images play a crucial role in the early detection of eye problems, aiding in timely diagnosis and treatment to prevent vision loss or blindness. With advancements in technology, Convolutional Neural Network (CNN) algorithms have emerged as effective tools for recognition, delineation, and classification tasks. This study proposes a comprehensive review of CNN algorithms used for retinal fundus image segmentation and classification. Our review follows a systematic approach, exploring diverse repositories to identify studies employing CNN to segment and classify retinal fundus images. Utilizing CNNs in the segmentation and classification of retinal fundus images can enhance the precision of segmentation outcomes and alleviate the sole dependence on human experts. This approach enables more accurate segmentation results, reducing the burden on human experts. A total of sixty-two studies are included in our review, analyzing aspects such as database usage and the advantages and disadvantages of the methods employed. The review provides valuable insights, limitations, observations, and future directions in the field. Despite certain limitations, the findings indicate that CNN algorithms consistently achieve high accuracies. The comprehensive examination of the included studies sheds light on the potential of CNN in retinal fundus image analysis.

Hoax news is a serious problem in this era. Many people are easily led by opinions made by certain people without seeing the truth or looking for existing facts. To overcome this, many researchers have conducted hoax news detection using various algorithms. In some studies, it is said that Random Forest has better performance to overcome this hoax news problem. In other studies, it is also said that CNN has the same level of performance as the Random Forest algorithm. In addition, the problem that is often found is the error in prediction due to improper preprocessing methods. Therefore, in this research, the appropriate preprocessing method is searched by using several preprocessing scenarios for the Convolutional Neural Network (CNN) and Random Forest algorithms. Therefore, in addition to finding the right preprocessing method for each algorithm, a performance comparison is also carried out on the CNN and Random Forest algorithms using a dataset of 4000 news facts from Kompas.com and 4000 hoax news from the turnback.hoax site. the results obtained in this study are random forest has an average model accuracy value of 90% and the CNN algorithm has an average model accuracy value of 60% using the same extraction method, namely TFIDF combined with Ngrams worth one or unigram

With the development of the Internet of Things (IoT) and virtual reality (VR) technology, the demand for high-precision gesture intelligent analysis of a human–machine interaction module for IoT–VR systems is increasing. Therefore, random forest (RF) and convolution neural network (CNN) algorithms are used in this study to build an intelligent gesture recognition model. The experiments were conducted to test the application performance of the design model. The test results show that the qualification rate of the analytical model designed in this study is significantly higher than that of the comparative model. When the threshold is determined to be 43.26 mm, the analytical qualification rates of the RF-CNN (the method of combining RF with CNN algorithms), faster regions with CNN features (Faster-RCNN), and RF models are 82.41%, 76.10%, and 59.10%, respectively. The calculation time of the RF–CNN model is between the two comparative models. From the test data, it can be observed that the research results have certain significance for improving the accuracy of gesture machine recognition technology in China’s VR Internet of Things (IoT) system.

The article is devoted to the problem of comparing the effectiveness of feedforward (FF) and convolutional neural networks (CNN) algorithms in the problems of handwritten digit recognition and classification. In recent years, the attention of many researchers to the FF and CNN algorithms has given rise to many hybrid models focused on solving specific problems. At the same time, the efficiency of each algorithm in terms of accuracy and labour intensity remains unclear. It is shown that in classical problems, FFs can have advantages over CNN in terms of labour intensity with the same accuracy of results. Using the handwritten digits data from the MNIST database as an example, it is shown that FF algorithms provide greater accuracy and require less computation time than CNN.

At present, location technology divides into outdoor location technology and indoor location technology. Comparing with other indoor location methods, ultra-wide band (UWB)indoor location technology has the advantages of strong anti-multipath interference ability and signal penetration ability. But, when this method faces large obstacles or metal obstacles, non line of sight (NLOS) will still occur. Indoor location system is a part of our project Home Care System for The Elderly. The NLOS of Indoor location system reduces the reliability of Home Care System for The Elderly. In order to improve the accuracy of location and efficiency of operation, an NLOS detection method based on k-nearest neighbors (KNN) algorithm is proposed. This algorithm modeled the training samples to find the K value with the highest recognition accuracy. The experimental results show that The NLOS detection accuracy of this method is 78% in the simulated apartment scene. With the same number of samples, the accuracy of the widely used Convolutional Neural Networks (CNN) algorithm is 67%. The experimental results show that KNN algorithm is more suitable for Home Care System for The Elderly for it can keep high accuracy with less requirement for the number of samples than CNN.

This research aims to address challenges in classifying types of banana using the Convolutional Neural Network (CNN) algorithm. The research background reflects the need for an automatic classification system to enhance the efficiency of banana farmers, considering that the manual methods still widely used have weaknesses in consistency and accuracy. Previous studies have successfully employed CNN for classifying various objects, including fruits. The CNN method is implemented using the VGG16 model training approach and prepared training data. This study focuses on three types of bananas—male, "kepok," and "muli"—with a specific emphasis on seed classification. Testing evaluates the model's accuracy, revealing a 78% accuracy rate. The application of the CNN algorithm can improve efficiency in classifying banana types. Despite achieving a 78% accuracy rate, the test results also indicate good values for precision (81%) and recall (78%). Therefore, the CNN algorithm can be considered an effective solution for automatically addressing issues in classifying banana types, contributing positively to banana farmers' productivity in Indonesia, especially when examining Accuracy, Precision, and Recall in percentage form.

A novel CNN algorithm for pathological complete response prediction using an I-SPY TRIAL breast MRI database

In order to improve the classification effect of the 3D CAD model, this paper combines the knowledge recognition algorithm of convolutional neural network to construct the 3D CAD model classification model. Moreover, this paper analyzes the application of the convolutional neural network algorithm in the classification of 3D CAD models, improves the knowledge recognition algorithm, and builds the basic structure of the convolutional neural network. In addition, this paper builds an intelligent recognition model with the support of the improved algorithm, designs experiments to verify the effect of this paper, and draws a visual display statistical graph based on the statistical test results. Through experimental research, it can be known that the 3D CAD classification model based on the knowledge recognition algorithm of convolutional neural network proposed in this paper can effectively improve the classification effect of the CAD model.

The rapid development of Internet technology has led to increasingly complex web service systems. The resulting large number of component interactions pose a challenge to anomaly detection, which is realized primarily by operation and maintenance (OM) personnel through the deployment, management, and monitoring of various key performance indicators (KPIs). Anomalous behaviors during daily OM often cause problems in KPI data; these problems include high noise, high dimensionality, and large-scale data streams. In addition, anomalies in KPI data occur infrequently and are of various types. These factors are the cause of the very low accuracy currently observed in conventional machine learning methods for detecting anomalies in large OM systems. Hence, a convolutional long short-term memory (C-LSTM) neural network is presented in this study to detect anomalies in small datasets that contain a variety of anomalies. First, a sliding window is used to preprocess the KPI data. Then, a C-LSTM neural network, which combines the features of the convolutional neural network (CNN) and LSTM algorithms, is employed to effectively model the time and numerical information contained in the preprocessed KPI data. Finally, the C-LSTM algorithm is tested on the datasets used in the competition of the Artificial Intelligence for Information Technology Operations (AIOPs) Active Network Management (ANM) 2018 Fall Project. The results show that the C-LSTM prediction algorithm outperforms the conventional LSTM and CNN algorithms in terms of its capacity to detect anomalies in small datasets that contain various anomalies, with a 12.90% higher accuracy, 5.68% higher recall, and 9.58% higher F1-score.

A traffic accident is an incident on the road that is unexpected and unintentional involving a vehicle with or without other road users that results in human casualties and loss of property value loss. The lack of awareness of the driver is a critical behavior in driving safety to deal with all possibilities that can occur while driving to maintain the safety of the driver and passengers. Several factors cause factors causing traffic accidents in Indonesia. The human factor is the most significant, with one example being using cell phones while driving. Previously, research on the introduction of human activities was conducted using several methods and approaches, but there are still several issues, such as a high false detection rate, inefficient architecture, and unsuitable data and feature use. As a result, the Convolutional Neural Network (CNN) algorithm was used in this study to classify human activities in images of car drivers using cell phones. This research collected image data from the Kaggle site, preprocessed data, classification using the CNN algorithm, and evaluation and analysis. The model has the highest accuracy of 99.4% in 5 classes of test data, safe driving, texting-right, talking on the phone-right, texting-left, and talking on the phone-left, with hyperparameter configuration, the number of batch size 16; learning rate 0.001; epochs 50; hyperbolic tangent activation function; the number of hidden neurons 32; and Adam optimizer, based on the test results and hyperparameter tuning using the Halving Grid Search. The training process requires 8.18 hours, with the best model training time at 137.66 seconds.

Controlling the spread of Coronavirus Disease 2019 (COVID-19) and reducing its impact on public health need prompt identification and treatment. To improve diagnostic accuracy, this study attempts to create and assess a Multi-Modality COVID-19 Diagnosis System that integrates X-ray, Electrocardiogram (ECG), and Computed Tomography (CT) images utilizing Convolutional Neural Network (CNN) algorithms. To increase the accuracy of COVID-19 diagnosis, the suggested system incorporates data from many imaging modalities in a novel way, including cardiac symptoms identified by ECG data. This approach has not been thoroughly studied in the literature to date. The system analyses CT, ECG, and X-ray images using CNN algorithms, including Visual Geometry Group 19 (VGG19) and Deep Convolutional Networks (DCNN). While ECG data helps detect related cardiac symptoms, CT and X-ray images offer precise insights into lung abnormalities indicative of COVID-19 pneumonia. Noise reduction and image smoothing are accomplished through the implementation of Gaussian filtering algorithms. After extracting characteristics suggestive of either bacterial or viral pneumonia, a deep neural network refines them for accurate COVID-19 identification. Python software is employed throughout the system's implementation. A thorough evaluation of the trained CNN model using separate datasets revealed an amazing 99.12% accuracy rate in COVID-19 detection from chest imaging data. The diagnostic accuracy of the suggested DCNN model was much higher than that of the current models, including Random Forest and Linear Ridge. The Multi-Modality COVID-19 Diagnosis System uses cutting-edge CNN algorithms to seamlessly combine ECG, X-ray, and CT imaging data to provide a highly accurate diagnosis tool. With the implementation of this approach, medical personnel could potentially be able to diagnose COVID-19 more quickly and accurately, which would improve the disease's treatment and control.

Insulators are one of the most used electrical equipment in the power system. Long-term exposure to the air environment can easily cause pollution flashovers and have a serious impact on the safe operation of the power transmission system. In this paper, after obtaining the discharge image of the insulator taken by the ultraviolet imager in the artificial pollution test, the image is preprocessed, and the convolutional neural network (CNN) algorithm is used to evaluate the pollution state of the insulator after reducing the image dimension. The results show that the pollution discharge status of insulators is positively correlated with their surface pollution and humidity. The average recognition accuracy of insulator pollution status obtained by the CNN algorithm can reach eighty-five percent, which can meet the requirements of engineering insulator pollution recognition.

BackgroundWalking depends on an intricate interplay between cognitive and motor functions that can be impaired by neurodegeneration. Recent developments on the use of wearable sensors permit the measurement of multiple gait markers to establish their association with symptoms of neurodegeneration. To estimate gait markers, accurate algorithms have been developed for sensors worn around the trunk (e.g., waist) or on lower limbs. However, there has been limited research to develop gait algorithms for wristband accelerometry, despite their superior wear compliance compared to other body‐worn sensors. Our work aims to investigate the feasibility of gait metrics estimated from wrist‐worn accelerometry devices.MethodHealthy adults (N=26) were recruited in a multiactivity study which also included a 25‐feet walking exercise along a corridor. Participants wore a lumbar wearable sensor (AX3, Axivity) and a wristband sensor on the non‐dominant hand (GENEActiv, Activinsights). Both devices were mechanically synchronised before and after each study session. Steps were detected and labelled with a heel‐impact detection algorithm applied on the lumbar sensor accelerometry data. Posterior visual inspection was implemented to correct for non‐steps or missing steps. Step labels were posteriorly used to train a convolutional neural network (CNN) algorithm to detect individual steps from the wristband device as well as standing and walking segments. Algorithm performance was assessed with a leave‐one‐subject‐out (LOSO) validation procedure.ResultThe LOSO validation resulted in an average global accuracy of 88% (95% confidence interval, CI: 87 – 90%) for the three labels of standing, walking, and steps. For the detection of individual steps, the percentage of absolute error was 6.5% (95% CI: 4.9 ‐ 8.0%), or an error of 6.5 steps per 100. The heel impact time error detection showed a mean delay of +9.7 msecs (95% CI: ‐2.2 ‐ 21.7 msecs).ConclusionOur investigation shows that it is feasible to assess gait from wristband devices using CNNs with high accuracy and an acceptable level of model generalisation. Our future work will focus on further improvements to our gait models, and on transferring the trained CNN models to estimate gait in populations affected by neurodegenerative conditions such as different forms of dementia, Parkinsonian disorders, and Huntington’s disease.